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OptScientist: Automated Discovery of Optimization Laws

Paper recorded by Signals 4 on 2026-10-20 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-20 on arXiv · recorded by Signals 4 on 2026-10-21

Category: cs.AI · 人工智能 · first seen 2026-10-21

Abstract

Many scientific phenomena are best understood as outcomes of underlying optimization problems, yet automated scientific discovery has largely focused on recovering equations that describe observed input-output patterns. We introduce OptScientist, a novel automated framework for discovering compact symbolic optimization laws directly from data. OptScientist comprises (i) a model that infers symboli

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#239 most recent of 960 cs.AI papers we have recorded · ↑ newer: Evolving Semantic Indexes for Generative Retrieval · ↓ older: Agentic Ensemble with Vision-Language Models for Visual Anomaly Detect

Cite this page: OptScientist: Automated Discovery of Optimization Laws: the #239 most recent of 960 cs.AI papers we have recorded (as of 2026-10-20). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/optscientist-automated-discovery-of-optimization-laws.html
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